Automation Technology and Human Performance: Current Research and Trends

analyses are continuing to examine the relationships between sleep apneas, real-time physiology measures,
and participant responses to workload transitions.

CONCLUSION

Increased computing power makes detailed physiological data collection possible, supporting
capabilities for adaptive automation in complex HMI tasks. Therefore, the primary limitations of linking real-
time physiological measures to operator performance capabilities are the complexities of the physiology-
performance relationships themselves. Links between physiology and task activity are identifiable, but there
are subtle multifactor relationships that depend heavily on the type of task performance and the dynamics
of task and workload transitions. With sufficient measurement and event resolutions, SCAMPI studies were
able to use as little as one minute of physiological data to predict significant variance in future task
performance from I to 10 minutes later. Important concerns resulting from SCAMPI projects include the preeminence of workload transitions and dynamic cognitive resource allocations, rather than vigilance
decrements, as issues for further study. Future system development must emphasize relating vectors of
physiology data to vectors of component task performance, based on the goals and success criteria of the
tasks themselves.

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